Comparison of Control Simulation on Cornstarch Dryer using
Mamdani and Sugeno Fuzzy Logic
Ulfah Mediaty Arief
1
, Dyah Nurani Setyaningsih
2
, Sugeng Purbawanto
1
, and Adi Setiawan
1
1
Electrical Engineering, Engineering Faculty, Universitas Negeri Semarang, Indonesia
2
Family Welfare Education, Engineering Faculty, Universitas Negeri Semarang, Indonesia
Keywords: Cornstarch, Dryer, Fuzzy Logic
Abstract: Corn is an important commodity for Indonesia. According to BPS data, Indonesia's maize production
increased by 2.81% annually. The abundance of corn productivity was making Techno Park Grobogan
innovated corn noodle. However, cornstarch is long dried and requires a lot of power to make its own loss in
this business. Fuzzy logic is one of the control algorithms that can streamline the working of the tool. The
weakness of previous researchs were using triangle membership function in the form. The purpose of this
study to applied a trapezoid-triangle membership function on the fuzzy logic simulation of Mamdani and
Sugeno method then comparing them in. The design of device to be simulated consists of input temperature
and humidity and the regulated output is heater, heat blower, and moisture blower. The simulation results
showed the Mamdani method is more efficient in using heat and power. While the mamdani method is
efficient in the dissemination of heat and time used.
1 INTRODUCTION
Several innovations to increase the selling value of
corn have been done. An innovation well developed
that is making corn noodles from Grobogan Techno
Park (Pusat Teknologi Agroteknologi. 2017). Corn
has the greatest carbohydrate content number 2 after
rice that is equal to 73,4% (Masniah and
Syamsuddin. 2013). Making corn noodle can not be
separated from the main ingredient that is corn flour.
The better the corn flour used is the better quality
corn noodles. The quality of corn flour based on
Indonesian National Standard (01-3727-1995) has a
smell, taste, and normal color with a maximum
water content of 10% (Badan Pengkajian and
Pengembangan Kebijakan Perdagang. 2016). Good
dried corn flour is used with moisture content of 14-
18% (Resmisari, 2014) (Wylis, et al. 2014). Good
flour is obtained through temperature and humidity
settings to avoid browning in the drying process.
Temperature required 50-55 ° C with 50% humidity
(Sacchetti, G., et al. 2004) (Ahmed, et. al. 2010).
This research purposed to simulate the efficient
control of efficient corn flour automatic control
system. In previous flour-drying innovations like,
cabinet dryer, rack dryer, and the latest is the rotary
dryer (Mentari, 2015). However, the control system
used has not been optimally consequently making
the drying time to be long, and the power used is
large. This research use fuzzy logic control method.
Fuzzy logic control has advantages over other
frequently used control methods such as
Proportional Integral Derivative (PID) and Artificial
Neural Network (ANN). Fuzzy control is more
robust than PID controllers, since fuzzy controls
cover a wider area of operation than a PID
controller. Fuzzy can worked with many disturbing
environments(Arief, et. al. 2015). The study was
conducted by comparing the simulation of Mamdani
fuzzy method with Sugeno method.
Fuzzy Logic Research is widely used in the field
of automatic control. Several studies on control
systems have developed fuzzy logic of temperature
and humidity in the dryer. Research conducted
(Benvenga, et al. 2010) showed good drying results
with a temperature of 54 ° C and humidity of 43%.
Arief, U., Setyaningsih, D., Purbawanto, S. and Setiawan, A.
Comparison of Control Simulation on Cornstarch Dryer using Mamdani and Sugeno Fuzzy Logic.
DOI: 10.5220/0009009602670272
In Proceedings of the 7th Engineering International Conference on Education, Concept and Application on Green Technology (EIC 2018), pages 267-272
ISBN: 978-989-758-411-4
Copyright
c
 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
267

Fuzzy logic has high experimental and predictive
values, so it is effective for designing and building a
control system on the dryer (Farzaneh, et al. 2016)
(Al-Mahasneh, et al. 2016). Other research got good
results after applying fuzzy logic to the dryer system
(Nugroho, et al. 2017) (Kumar, et. al., 2013)
(Mansor, H., et al. 2010). The research is still use the
triangle membership function. The model has its
weaknesses, namely the integral squared error is
quite high, as a result the time to reach the setpoint
is long enough (Suratno, et al. 2011).
This research was carried out innovation on
member function form and treatment of output
control on rule base. The member function used is
trapezoid-triangular. Member functions from one
input and another input also have the same form.
This study uses a combination of member function
forms between triangles and trapezoidal. The
combination of membership functions in the form of
a trapezoid and a triangle is the best form
combination (Barua, et al. 2007) (Coupland, 2007)
(Butt, et al. 2004) (Zhao, et al. 2002). The
combination of trapezoid and triangle gives fuzzy
variables with sharp input. This combination shows
the difference between the linguistic set points. The
combination expresses the signal measured by the
sensor properly
2 METHOD
2.1 Basic Stucture of Product
The basic structure of the design of the device to be
simulated consists of heater, and 2 blowers with
fuzzy logic control. A good algorithm control is
created through an overview of the system to be
controlled. The system is designed with 2 inputs of
the DHT 22 sensor form temperature and humidity.
The DHT 22 sensor will detected the temperature
and humidity present in the drying chamber. The
input is processed by microcontoler Atmega328
using fuzzy logic. The output of a microcontroller is
a PWM signal to regulated the output of this system.
The heater in this scheme serves as a drying
temperature controller. The temperature in the space
will be propagated by the hot wind blower. While
the other blower serves to provide a moist air flow
from outside the dryer room.
Figure 1: Dryer Block diagram.
2.2 Design of Fuzzy Logic Algorithm
The test is performed by performing a
comparison analysis of fuzzy logic control
simulation of Mamdani and Sugeno method using
Matlab application. Fuzzy logic itself consists of 4
stages that is, Fuzzifier, Rules, Inference, dan
Deffuzifier (Khaur, et al. 2012) (Singhala, et al.
2014)(Chaudhari, et al 2014). The input of DHT22
sensor will be used to design the fuzzifier, inferences
engine, Rule Base, then in the defuzzifier to achieve
the quality and quantity on the machine used
(Abbas, et al 2011).. This work design using
trapezoidal membership function for input
temperature with a range of 0 ° C-100 ° C. While the
input humidity has a membership function in the
form of a triangle with a range of 0% -100%. Every
input is grouped into three conditions as in Table 1
for temperature and Table 2 for humidity.
Table 1: Temperature Membership Group
Specification Fuzzy Level
Index
0-30 Dingin D
20-70 Sedang S
60-100 Panas P
Table 2: Moisture Membership Group
Specification Fuzzy Level Index
0-30 Tidak Lembab TL
20-70 Lembab L
60-100 Sangat Lembab SL
EIC 2018 - The 7th Engineering International Conference (EIC), Engineering International Conference on Education, Concept and
Application on Green Technology
268

Each input variable has three membership
functions can be seen in Figure 2a and Figure 2b.
The temperature membership function consists of,
“dingin”, “sedang”, dan “panas”. As for the
membership function of humidity consists of, “tidak
lembab”, “lembab”, dan “sangat lembab”
(a)
(b)
Figure 2: Plot of Membership Function (a) The
Temperature Variable, (b) The Humidity Variable.
The input is then received by the machine
inference with the AND logic operator. After that,
input will processed microcontroller using fuzzy
logic rule base. The number of Rules Base of the
system can be searched by the formula m^n, with m
= the maximum number of members is 3. While n =
the number of inputs that is 2. So, the number of rule
base used a number of 9 rules. The details are as
follows:
Table 3: Rule Base Of Control Fuzzy Logic.
Tempera-
ture
Humi-
dity
Heater Blower
1
Blower
2
Dingin TL P K1 SP2
Dingin TL P K1 P2
Dingin TL P K1 K2
Sedang L H P1 SP2
Sedang L H P1 P2
Sedang L H P1 K2
Panas SL M SP1 SP2
Panas SL M SP1 P2
Panas SL M SP1 K2
3 RESULT AND DISCUSSION
The result of simulation test is done using Matlab
(Matrix Laboratory) R2014a application. Matlab has
ability to simulate various mathematical
calculations. This test phase uses two methods of
fuzzy logic namely Mamdani, and Sugeno. The test
is done by setting the system input. Each method
will be compared to the simulation result.
(a)
Comparison of Control Simulation on Cornstarch Dryer using Mamdani and Sugeno Fuzzy Logic
269

(b)
Figure 3: Display Logic Fuzzy (A) Mamdani Method, (B)
Method Sugeno.
Each fuzzy method in this test will see the output
response generated. Each method is given input,
form member function, and same rule base. So, we
can see the difference of working system by them.
The defuzzification process of Mamdani method
using centroid method. While, Sugeno method using
wtaver method. After going through the
defuzzification stage will be known output
produced.
The following is the comparative test result:
Table 4: Simulation Result Of Mamdani Method.
Dry Room Fuzzy Output
Tempera-
ture
Humi-
dity
Heater Warm
Blower
Moist
Blower
35 30 55 20 24.7
35 40 55 20 24.5
35 50 55 20 24.5
35 60 55 20 24.7
40 30 55 20 24.7
40 40 55 20 24.5
40 50 55 20 24.5
40 60 55 20 24.7
45 30 55 20 24.7
45 40 55 20 24.5
45 50 55 20 24.5
45 60 55 20 24.7
50 30 55 20 24.7
50 40 55 20 24.5
50 50 55 20 24.5
50 60 55 20 24.7
55 30 55 20 24.7
55 40 55 20 24.5
55 50 55 20 24.5
55 60 55 20 24.7
60 30 55 20 24.7
60 40 55 20 24.6
60 50 55 20 24.6
60 60 55 20 24.7
Table 5: Simulation Result Of Sugeno Method.
Dry Room Fuzzy Output
Tempera-
ture
Humi-
dity
Heater Warm
Blower
Moist
Blower
35 30 60 30
30
35 40 60 30
30
35 50 60 30
30
35 60 60 30
30
40 30 60 30
30
40 40 60 30
30
40 50 60 30
30
40 60 60 30
30
45 30 60 30
30
45 40 60 30
30
45 50 60 30
30
45 60 60 30
30
50 30 60 30
30
50 40 60 30
30
50 50 60 30
30
50 60 60 30
30
55 30 60 30
30
55 40 60 30
30
55 50 60 30
30
55 60 60 30
30
60 30 60 30 30
60 40 60 30 30
60 50 60 30 30
60 60 60 30 30
Results from the simulation table obtained fuzzy
logic mamdani and sugeno method can keep the
output to still maintain the conditions specified. The
mamdani method can optimize the temperature of
heater and the power used is not large. However, it
takes quite a long time than the sugeno method.
While the sugeno method is able to use the heater
efficiently with high temperatures. Drying time is
relatively fast. But the power needed is great. The
difference will be more clear on the graphs produced
by each method.
(a)
EIC 2018 - The 7th Engineering International Conference (EIC), Engineering International Conference on Education, Concept and
Application on Green Technology
270

(b)
Figure 4: Graphic Result (a) Mamdani Method, (b)
Sugeno Method
Mamdani method commonly known as min-max
method. The antecedents of the mamdani method
have a minimum form, while the combined
consequences have the maximum shape. Every rules
in the mamdani method is implication (causal). In
addition, the rules set of mamdani method are also
independent of each other (Setiadji, 2009). Thus, in
the resulting graph shown input from temperature
and humidity is small then the heater will work
optimally to supply heat.
The sugeno method is a fuzzy inference method
with representation of IF-THEN-shaped rules. The
outputs generated at the rule base stages are
constants or linear equations (Syarif, 2016)
(Wachdani, 2010). Collection and correlation
between rules will shape the inferences. Then, in the
defuzzification stage will be searched the average
value (Kusumadewi,2010). The resulting output is
crisp.
4 CONCLUSIONS
The result of the simulation shows that there is no
significant difference. The mamdani method is
efficient in the use of electrical power with average
system workability. So, the drying time will be
slightly longer than sugeno method. While the
method sugeno able to make the system work in
incentives with the resulting power consequences
will be higher. Therefore, the use of fuzzy logic
control method mamdani or sugeno will be better if
adjusting the goals and desires to be achieved. If you
want fast time use the Sugeno method. But, if you
want to save power, it is better to choose mamdani
method.
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Application on Green Technology
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